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      "name": "Section 2.2.6 GARCH",
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            "Installing yfinance and arch and getting the data...\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
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            "text/plain": [
              "                   Open         High          Low        Close    Adj Close  \\\n",
              "Date                                                                          \n",
              "1957-03-04    44.060001    44.060001    44.060001    44.060001    44.060001   \n",
              "1957-03-05    44.220001    44.220001    44.220001    44.220001    44.220001   \n",
              "1957-03-06    44.230000    44.230000    44.230000    44.230000    44.230000   \n",
              "1957-03-07    44.209999    44.209999    44.209999    44.209999    44.209999   \n",
              "1957-03-08    44.070000    44.070000    44.070000    44.070000    44.070000   \n",
              "...                 ...          ...          ...          ...          ...   \n",
              "2022-03-23  4493.100098  4501.069824  4455.810059  4456.240234  4456.240234   \n",
              "2022-03-24  4469.979980  4520.580078  4465.169922  4520.160156  4520.160156   \n",
              "2022-03-25  4522.910156  4546.029785  4501.069824  4543.060059  4543.060059   \n",
              "2022-03-28  4541.089844  4552.750000  4517.689941  4540.910156  4540.910156   \n",
              "2022-03-29  4602.859863  4627.629883  4589.660156  4598.450195  4598.450195   \n",
              "\n",
              "                  Volume    Return  \n",
              "Date                                \n",
              "1957-03-04  1.890000e+06  0.731595  \n",
              "1957-03-05  1.860000e+06  0.363141  \n",
              "1957-03-06  1.840000e+06  0.022610  \n",
              "1957-03-07  1.830000e+06 -0.045219  \n",
              "1957-03-08  1.630000e+06 -0.316669  \n",
              "...                  ...       ...  \n",
              "2022-03-23  4.014360e+09 -1.227270  \n",
              "2022-03-24  3.573430e+09  1.434391  \n",
              "2022-03-25  3.577520e+09  0.506617  \n",
              "2022-03-28  3.696850e+09 -0.047323  \n",
              "2022-03-29  1.249621e+09  1.267148  \n",
              "\n",
              "[16382 rows x 7 columns]"
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              "      <th>Open</th>\n",
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              "      <th>1957-03-04</th>\n",
              "      <td>44.060001</td>\n",
              "      <td>44.060001</td>\n",
              "      <td>44.060001</td>\n",
              "      <td>44.060001</td>\n",
              "      <td>44.060001</td>\n",
              "      <td>1.890000e+06</td>\n",
              "      <td>0.731595</td>\n",
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              "      <th>1957-03-05</th>\n",
              "      <td>44.220001</td>\n",
              "      <td>44.220001</td>\n",
              "      <td>44.220001</td>\n",
              "      <td>44.220001</td>\n",
              "      <td>44.220001</td>\n",
              "      <td>1.860000e+06</td>\n",
              "      <td>0.363141</td>\n",
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              "      <th>1957-03-06</th>\n",
              "      <td>44.230000</td>\n",
              "      <td>44.230000</td>\n",
              "      <td>44.230000</td>\n",
              "      <td>44.230000</td>\n",
              "      <td>44.230000</td>\n",
              "      <td>1.840000e+06</td>\n",
              "      <td>0.022610</td>\n",
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              "      <th>1957-03-07</th>\n",
              "      <td>44.209999</td>\n",
              "      <td>44.209999</td>\n",
              "      <td>44.209999</td>\n",
              "      <td>44.209999</td>\n",
              "      <td>44.209999</td>\n",
              "      <td>1.830000e+06</td>\n",
              "      <td>-0.045219</td>\n",
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              "      <th>1957-03-08</th>\n",
              "      <td>44.070000</td>\n",
              "      <td>44.070000</td>\n",
              "      <td>44.070000</td>\n",
              "      <td>44.070000</td>\n",
              "      <td>44.070000</td>\n",
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              "      <td>-0.316669</td>\n",
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              "      <th>2022-03-23</th>\n",
              "      <td>4493.100098</td>\n",
              "      <td>4501.069824</td>\n",
              "      <td>4455.810059</td>\n",
              "      <td>4456.240234</td>\n",
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              "      <td>4469.979980</td>\n",
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              "      <td>4465.169922</td>\n",
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          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "print(\"Installing yfinance and arch and getting the data...\")\n",
        "!pip install arch 1>/dev/null\n",
        "!pip install yfinance 1>/dev/null\n",
        "from yfinance import download\n",
        "import pandas as pd\n",
        "import numpy as np ;\n",
        "import matplotlib.pyplot as pl\n",
        "from statsmodels.base.model import GenericLikelihoodModel\n",
        "from datetime import datetime\n",
        "zero,one,two,five,ten,hundred=0e0,1e0,2e0,5e0,1e1,1e2 # some friendly numbers\n",
        "half,GoldenRatio=one/two,(one+np.sqrt(five))/two\n",
        "\n",
        "# get the daily returns of the S&P 500 \n",
        "SPX=download('^GSPC','1957-03-01').dropna()\n",
        "SPX['Return']=SPX['Adj Close'].pct_change()*hundred\n",
        "SPX.index=pd.DatetimeIndex(SPX.index).to_period('D')\n",
        "SPX.dropna(inplace=True)\n",
        "SPX.loc[SPX[\"Volume\"]==0,\"Volume\"]=np.nan\n",
        "SPX"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit a GARCH model using arch package\n",
        "print(\"Installing ARCH and fitting model with Normal innovations.\")\n",
        "from arch.univariate import ConstantMean, GARCH, Normal\n",
        "\n",
        "model = ConstantMean(SPX[\"Return\"])\n",
        "model.volatility = GARCH(1, 0, 1)\n",
        "model.distribution = Normal()\n",
        "fit=model.fit(update_freq=0)\n",
        "print(fit.summary())\n",
        "SPX[\"Volatility\"]=fit.conditional_volatility\n",
        "SPX[\"Innovations\"]=SPX[\"Return\"]/SPX[\"Volatility\"]\n",
        "\n",
        "# fit normal pdf to plot\n",
        "from scipy.stats import norm as density,jarque_bera\n",
        "params=density.fit(SPX[\"Innovations\"])\n",
        "print(\"Fitted Normal distribution: %g,%g\" % params)\n",
        "test=jarque_bera(SPX[\"Innovations\"])\n",
        "print(\"Jarque-Bera Test results: %g,%g\" % test)\n",
        "\n",
        "# Figure 2.8\n",
        "figure,plot=pl.subplots(figsize=(ten*GoldenRatio,ten))\n",
        "counts,bins,patches=plot.hist(SPX[\"Innovations\"],bins=np.linspace(-five,five,100),label='Data')\n",
        "plot.plot(bins,density.pdf(bins)*(max(bins)-min(bins))/(len(bins)-1)*sum(counts),'-r',label=\"\"\"Entries %d %s to %s\n",
        "Jarque-Bera  %12.6f %12.6f\n",
        "$\\\\hat\\\\mu$            %12.6f \n",
        "$\\\\hat\\\\sigma$            %12.6f\"\"\" % (\n",
        "    sum(counts),min(SPX.index),max(SPX.index),\n",
        "    test[0],test[1],\n",
        "    params[0],\n",
        "    params[1]\n",
        "))\n",
        "plot.axvline(color='black',lw=1)\n",
        "plot.set_xlabel(\"Innovations\",fontsize=14)\n",
        "plot.set_ylabel(\"Frequency\",fontsize=14)\n",
        "figure.suptitle(\"\\nFit of GARCH(1,1) Model to S&P 500 Index\",fontsize=22)\n",
        "plot.set_title(\"Driver: Returns; Distribution: Normal; Period: %s to %s\" % (min(SPX.index),max(SPX.index)),fontsize=18)\n",
        "pl.setp(plot.legend(loc='best',fontsize=12).texts,family='monospace');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wNqwBZ61d0v7",
        "outputId": "7b0c50f7-dcd9-4395-a7f1-030d19239de2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing ARCH and fitting model with Normal innovations.\n",
            "Optimization terminated successfully.    (Exit mode 0)\n",
            "            Current function value: 19936.335407449245\n",
            "            Iterations: 12\n",
            "            Function evaluations: 90\n",
            "            Gradient evaluations: 12\n",
            "                     Constant Mean - GARCH Model Results                      \n",
            "==============================================================================\n",
            "Dep. Variable:                 Return   R-squared:                       0.000\n",
            "Mean Model:             Constant Mean   Adj. R-squared:                  0.000\n",
            "Vol Model:                      GARCH   Log-Likelihood:               -19936.3\n",
            "Distribution:                  Normal   AIC:                           39880.7\n",
            "Method:            Maximum Likelihood   BIC:                           39911.5\n",
            "                                        No. Observations:                16382\n",
            "Date:                Tue, Mar 29 2022   Df Residuals:                    16381\n",
            "Time:                        16:54:23   Df Model:                            1\n",
            "                                 Mean Model                                 \n",
            "============================================================================\n",
            "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
            "----------------------------------------------------------------------------\n",
            "mu             0.0551  5.825e-03      9.456  3.195e-21 [4.366e-02,6.649e-02]\n",
            "                              Volatility Model                              \n",
            "============================================================================\n",
            "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
            "----------------------------------------------------------------------------\n",
            "omega          0.0103  2.002e-03      5.155  2.534e-07 [6.397e-03,1.425e-02]\n",
            "alpha[1]       0.0949  1.063e-02      8.926  4.398e-19   [7.406e-02,  0.116]\n",
            "beta[1]        0.8968  1.067e-02     84.023      0.000     [  0.876,  0.918]\n",
            "============================================================================\n",
            "\n",
            "Covariance estimator: robust\n",
            "Fitted Normal distribution: 0.0379686,0.999154\n",
            "Jarque-Bera Test results: 5512.04,0\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 1164.98x720 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "9RZs0eXxxj-s"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}
